Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment
文献类型：期刊论文
| 作者 | Wen-Han Zhu1,2; Wei Sun
|
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:2页码:204-218 |
| 关键词 | Image quality assessment (IQA) no-reference (NR) structural computational modeling human visual system visual feature extraction |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-020-1270-z |
| 英文摘要 | Objective image quality assessment (IQA) plays an important role in various visual communication systems, which can automatically and efficiently predict the perceived quality of images. The human eye is the ultimate evaluator for visual experience, thus the modeling of human visual system (HVS) is a core issue for objective IQA and visual experience optimization. The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively, while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity. For bridging the gap between signal distortion and visual experience, in this paper, we propose a novel perceptual no-reference (NR) IQA algorithm based on structural computational modeling of HVS. According to the mechanism of the human brain, we divide the visual signal processing into a low-level visual layer, a middle-level visual layer and a high-level visual layer, which conduct pixel information processing, primitive information processing and global image information processing, respectively. The natural scene statistics (NSS) based features, deep features and free-energy based features are extracted from these three layers. The support vector regression (SVR) is employed to aggregate features to the final quality prediction. Extensive experimental comparisons on three widely used benchmark IQA databases (LIVE, CSIQ and TID2013) demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44017]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China 2.MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China |
| 推荐引用方式 GB/T 7714 | Wen-Han Zhu,Wei Sun. Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment[J]. International Journal of Automation and Computing,2021,18(2):204-218. |
| APA | Wen-Han Zhu,&Wei Sun.(2021).Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment.International Journal of Automation and Computing,18(2),204-218. |
| MLA | Wen-Han Zhu,et al."Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment".International Journal of Automation and Computing 18.2(2021):204-218. |
入库方式： OAI收割
来源：自动化研究所
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